Digitalization and sector coupling enable companies to turn into flexumers. By using the flexibility of their multi-energy system (MES), they reduce costs and carbon emissions while balancing the electricity grid. …
Digitalization and sector coupling enable companies to turn into flexumers. By using the flexibility of their multi-energy system (MES), they reduce costs and carbon emissions while balancing the electricity grid. However, to identify the necessary investments in energy conversion and storage technologies to leverage demand response (DR) potentials, companies need to assess the value of flexibility. Therefore, this study quantifies the flexibility value of a production company's MES by optimizing the synthesis, design, and operation of a decarbonizing MES considering self-consumption optimization, peak shaving, and integrated DR based on hourly prices and carbon emission factors (CEFs). The detailed case study of a beverage company in northern Germany considers vehicle-to-X of electrical industrial forklifts, power-to-heat on multiple temperatures, wind turbines, photovoltaic systems, and energy storage systems (thermal, electrical, and hydrogen). We propose and apply novel data-driven metrics to evaluate the intensity of price-based and CEF-based DR. The results reveal that flexibility usage reduces decarbonization costs (by 19%–80% depending on electricity and carbon removal prices), total annual costs, operational carbon footprint, energy-weighted average prices and CEFs, and fossil energy dependency. The results also suggest that a net-zero operational carbon emission MES requires flexibility, which, in an economic case, is provided by a combination of different flexible technologies and storage systems that complement each other. While the value of flexibility depends on various market and consumer-specific factors such as electricity or carbon removal prices, this study highlights the importance of demand flexibility for the decarbonization of MESs.
Digitalization and sector coupling enable companies to turn into flexumers. By using the flexibility of their multi-energy system (MES), they reduce costs and carbon emissions while stabilizing the electricity system. …
Digitalization and sector coupling enable companies to turn into flexumers. By using the flexibility of their multi-energy system (MES), they reduce costs and carbon emissions while stabilizing the electricity system. However, to identify the necessary investments in energy conversion and storage technologies to leverage demand response (DR) potentials, companies need to assess the value of flexibility. Therefore, this study quantifies the flexibility value of a production company's MES by optimizing the synthesis, design, and operation of a decarbonizing MES considering self-consumption optimization, peak shaving, and integrated DR based on hourly prices and carbon emission factors (CEFs). The detailed case study of a beverage company in northern Germany considers vehicle-to-X of powered industrial trucks, power-to-heat on multiple temperatures, wind turbines, photovoltaic systems, and energy storage systems (thermal energy, electricity, and hydrogen). We propose and apply novel data-driven metrics to evaluate the intensity of price-based and CEF-based DR. The results reveal that flexibility usage reduces decarbonization costs (by 19-80% depending on electricity and carbon removal prices), total annual costs, operating carbon emissions, energy-weighted average prices and CEFs, and fossil energy dependency. The results also suggest that a net-zero operational carbon emission MES requires flexibility, which, in an economic case, is provided by a combination of different flexible technologies and storage systems that complement each other. While the value of flexibility depends on various market and consumer-specific factors such as electricity or carbon removal prices, this study highlights the importance of demand flexibility for the decarbonization of MESs.
Price-based demand response (PBDR) has recently been attributed great economic but also environmental potential. However, the determination of its short-term effects on carbon emissions requires the knowledge of marginal emission …
Price-based demand response (PBDR) has recently been attributed great economic but also environmental potential. However, the determination of its short-term effects on carbon emissions requires the knowledge of marginal emission factors (MEFs), which compared to grid mix emission factors (XEFs), are cumbersome to calculate due to the complex characteristics of national electricity markets. This study, therefore, proposes two merit order-based methods to approximate hourly MEFs and applies them to readily available datasets from 20 European countries for the years 2017–2019. Based on the calculated electricity prices, standardized daily load shifts were simulated which indicated that carbon emissions increased for 8 of the 20 countries and by 2.1% on average. Thus, under specific circumstances, PBDR leads to carbon emissions increases, mainly due to the economic advantage fuel sources such as lignite and coal have in the merit order. MEF-based load shifts reduced the mean resulting carbon emissions by 35%, albeit with 56% lower monetary cost savings compared to price-based load shifts. Finally, by repeating the load shift simulations for different carbon price levels, the impact of the carbon price on the resulting carbon emissions was analyzed. The Spearman correlation coefficient between carbon intensity and marginal cost along the German merit order substantially increased with increasing carbon price. The coefficients were -0.13 for the 2019 carbon price of 24.9 €/t, 0 for 42.6 €/t, and 0.4 for 100.0 €/t. Therefore, with adequate carbon prices, PBDR can be an effective tool for both economical and environmental improvement.
This work presents a whole-year simulation study on nonlinear mixed-integer Model Predictive Control (MPC) for a complex thermal energy supply system which consists of a heat pump, stratified water storages, …
This work presents a whole-year simulation study on nonlinear mixed-integer Model Predictive Control (MPC) for a complex thermal energy supply system which consists of a heat pump, stratified water storages, free cooling facilities, and a large underground thermal storage. For solution of the arising Mixed-Integer Non-Linear Programs (MINLPs) we apply an existing general and optimal-control-suitable decomposition approach. To compensate deviation of forecast inputs from measured disturbances, we introduce a moving horizon estimation step within the MPC strategy. The MPC performance for this study, which consists of more than 50,000 real time suitable MINLP solutions, is compared to an elaborate conventional control strategy for the system. It is shown that MPC can significantly reduce the yearly energy consumption while providing a similar degree of constraint satisfaction, and autonomously identify previously unknown, beneficial operation modes.
This work presents a whole-year simulation study on nonlinear mixed-integer Model Predictive Control (MPC) for a complex thermal energy supply system which consists of a heat pump, stratified water storages, …
This work presents a whole-year simulation study on nonlinear mixed-integer Model Predictive Control (MPC) for a complex thermal energy supply system which consists of a heat pump, stratified water storages, free cooling facilities, and a large underground thermal storage. For solution of the arising Mixed-Integer Non-Linear Programs (MINLPs) we apply an existing general and optimal-control-suitable decomposition approach. To compensate deviation of forecast inputs from measured disturbances, we introduce a moving horizon estimation step within the MPC strategy. The MPC performance for this study, which consists of more than 50,000 real time suitable MINLP solutions, is compared to an elaborate conventional control strategy for the system. It is shown that MPC can significantly reduce the yearly energy consumption while providing a similar degree of constraint satisfaction, and autonomously identify previously unknown, beneficial operation modes.
Abstract The collocation method meshed with non-linear programming techniques provides an efficient strategy for the numerical solution of optimal control problems. Good accuracy can be obtained for the state and …
Abstract The collocation method meshed with non-linear programming techniques provides an efficient strategy for the numerical solution of optimal control problems. Good accuracy can be obtained for the state and the control trajectories as well as for the value of the objective function. In addition, the control strategy can be quite flexible in form. However, it is necessary to select the appropriate number of collocation points and number of parameters in the approximating functions with care.
Electricity accounts for 25% of global greenhouse gas emissions. Reducing emissions related to electricity consumption requires accurate measurements readily available to consumers, regulators and investors. In this case study, we …
Electricity accounts for 25% of global greenhouse gas emissions. Reducing emissions related to electricity consumption requires accurate measurements readily available to consumers, regulators and investors. In this case study, we propose a new real-time consumption-based accounting approach based on flow tracing. This method traces power flows from producer to consumer thereby representing the underlying physics of the electricity system, in contrast to the traditional input-output models of carbon accounting. With this method we explore the hourly structure of electricity trade across Europe in 2017, and find substantial differences between production and consumption intensities. This emphasizes the importance of considering cross-border flows for increased transparency regarding carbon emission accounting of electricity.
An optimized heat pump control for building heating was developed for minimizing CO 2 emissions from related electrical power generation. The control is using weather and CO 2 emission forecasts …
An optimized heat pump control for building heating was developed for minimizing CO 2 emissions from related electrical power generation. The control is using weather and CO 2 emission forecasts as inputs to a Model Predictive Control (MPC)—a multivariate control algorithm using a dynamic process model, constraints and a cost function to be minimized. In a simulation study, the control was applied using weather and power grid conditions during a full-year period in 2017–2018 for the power bidding zone DK2 (East, Denmark). Two scenarios were studied; one with a family house and one with an office building. The buildings were dimensioned based on standards and building codes/regulations. The main results are measured as the CO 2 emission savings relative to a classical thermostatic control. Note that this only measures the gain achieved using the MPC control, that is, the energy flexibility, not the absolute savings. The results show that around 16% of savings could have been achieved during the period in well-insulated new buildings with floor heating. Further, a sensitivity analysis was carried out to evaluate the effect of various building properties, for example, level of insulation and thermal capacity. Danish building codes from 1977 and forward were used as benchmarks for insulation levels. It was shown that both insulation and thermal mass influence the achievable flexibility savings, especially for floor heating. Buildings that comply with building codes later than 1979 could provide flexibility emission savings of around 10%, while buildings that comply with earlier codes provided savings in the range of 0–5% depending on the heating system and thermal mass.
Price-based demand response (PBDR) has recently been attributed great economic but also environmental potential. However, the determination of its short-term effects on carbon emissions requires the knowledge of marginal emission …
Price-based demand response (PBDR) has recently been attributed great economic but also environmental potential. However, the determination of its short-term effects on carbon emissions requires the knowledge of marginal emission factors (MEFs), which compared to grid mix emission factors (XEFs), are cumbersome to calculate due to the complex characteristics of national electricity markets. This study, therefore, proposes two merit order-based methods to approximate hourly MEFs and applies them to readily available datasets from 20 European countries for the years 2017–2019. Based on the calculated electricity prices, standardized daily load shifts were simulated which indicated that carbon emissions increased for 8 of the 20 countries and by 2.1% on average. Thus, under specific circumstances, PBDR leads to carbon emissions increases, mainly due to the economic advantage fuel sources such as lignite and coal have in the merit order. MEF-based load shifts reduced the mean resulting carbon emissions by 35%, albeit with 56% lower monetary cost savings compared to price-based load shifts. Finally, by repeating the load shift simulations for different carbon price levels, the impact of the carbon price on the resulting carbon emissions was analyzed. The Spearman correlation coefficient between carbon intensity and marginal cost along the German merit order substantially increased with increasing carbon price. The coefficients were -0.13 for the 2019 carbon price of 24.9 €/t, 0 for 42.6 €/t, and 0.4 for 100.0 €/t. Therefore, with adequate carbon prices, PBDR can be an effective tool for both economical and environmental improvement.